ktra kinang3

#install.packages("reticulate")
library(reticulate)

## Warning: package 'reticulate' was built under R version 4.4.3

use_python("C:/Users/Admin/anaconda3/python.exe", required = TRUE)

#bai1
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error
iris = sns.load_dataset("iris")
X = iris[['petal_length']]  
y = iris['petal_width']   
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LinearRegression()
model.fit(X_train, y_train)

LinearRegression()

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y_pred = model.predict(X_test)
mae = mean_absolute_error(y_test, y_pred)
print(f'MAE: {mae:.4f}')

## MAE: 0.1682

plt.scatter(X_test, y_test, label="Actual", color="blue")
plt.plot(X_test, y_pred, label="Predicted", color="red")
plt.xlabel("Petal Length")
plt.ylabel("Petal Width")
plt.legend()
plt.show()

library(readr)

## Warning: package 'readr' was built under R version 4.4.3

heart <- read_csv("heart.csv")

## Rows: 303 Columns: 14
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (14): age, sex, cp, trestbps, chol, fbs, restecg, thalach, exang, oldpea...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

View(heart)

import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report
heart = pd.read_csv("heart.csv")  
X = heart.drop(columns=['target'])  
y = heart['target']               
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

LogisticRegression(max_iter=1000)

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred)
recall = recall_score(y_test, y_pred)
from sklearn.metrics import accuracy_score

accuracy = accuracy_score(y_test, y_pred)  
precision = precision_score(y_test, y_pred) 
recall = recall_score(y_test, y_pred)  

print(f'Accuracy: {accuracy:.4f}')

## Accuracy: 0.8852

print(f'Precision: {precision:.4f}')

## Precision: 0.8788

print(f'Recall: {recall:.4f}')

## Recall: 0.9062

ktra kinang3

Lâm Ngọc Hải

2025-03-13